Adrian Röfer, Russell Buchanan, Maximilian Argus, Sethu Vijayakumar and Abhinav Valada, Efficient Learning of Object Placement with Intra-Category Transfer, IEEE Robotics and Automation Letters (RAL), vol. xx(yy), pp. xxxx-yyyy (2025). [pdf] [DOI] [video] [citation]
Efficient learning from demonstration for longhorizon tasks remains an open challenge in robotics. While significant effort has been directed toward learning trajectories, a recent resurgence of object-centric approaches has demonstrated
improved sample efficiency, enabling transferable robotic skills. Such approaches model tasks as a sequence of object poses over time. In this work, we propose a scheme for transferring observed object arrangements to novel object instances by
learning these arrangements on canonical class frames. We then employ this scheme to enable a simple yet effective approach for training models from as few as five demonstrations to predict arrangements of a wide range of objects including tableware, cutlery, furniture, and desk spaces. We propose a method for optimizing the learned models to enable efficient learning of tasks such as setting a table or tidying up an office with intra-category transfer, even in the presence of distractors. We present extensive experimental results in simulation and on a real robotic system for table setting which, based on human evaluations, scored 73.3% compared to a human baseline.
We make the code and trained models publicly available at https://oplict.cs.uni-freiburg.de.